Neighborhood Deprivation, Race and Ethnicity, and Prostate Cancer Outcomes Across California Health Care Systems

This cohort study compares all-cause mortality by neighborhood deprivation and race and ethnicity among individuals with prostate cancer receiving care in the US Department of Veterans Affairs (VA) health care system vs those receiving care outside the VA.


Geomasking address-level geocodes to protect confidentiality
In order to protect confidentiality and abide by privacy regulations governing use of CCR data for research, CCR participant address geocodes at time of diagnosis were geomasked using a random spatial displacement 1,2 .This procedure involved setting a radius around each address geocode and randomly displacing the geocode from the origin based on a Gaussian distribution with 3 standard deviations set to 400m, which was deemed satisfactory for protecting confidentiality within the registry's guidelines.In addition, the direction (north or south for latitude, east or west for longitude) of displacement was assigned from a random Bernoulli distribution.This process was repeated for every observation in the database.While this procedure was different from the approach for geocoding in the VA, which used actual addresses, the spatial displacement was restricted to a 400m radius surrounding the participant's actual address, limiting potential misclassification of census tract.Evaluations comparing different geomasking approaches suggest that spatial displacement within a small geographic area provide an appropriate balance between privacy protection while preserving fidelity of exposure assessment 3 .

Sensitivity analysis for confounding by measured demographic, clinical and geographic factors
As a sensitivity analysis to evaluate impact of confounding by study population characteristics that differ between the Greater Los Angeles Veterans Administration cohort and the California Cancer Registry population-based cohort, we conducted a refined matched cohort design.We identified strata of age (10-year categories), race and ethnicity (Non-Hispanic White, Non-Hispanic Black, Other), stage at diagnosis (Localized, Regional/Distant), Year of diagnosis (2000-2004, 2005-2009, 2010-2015, 2016-2018), and census tract in the Greater Los Angeles Veterans Administration cohort of men with prostate cancer and used these strata to filter the California Cancer Registry population-based cohort.Only men in strata with ≥1 man with prostate cancer were retained.Following this matching procedure, there were 2,914 men with prostate cancer who remained in the same strata.We refer to this matched cohort as the "fully matched" cohort.

Statistical tests for heterogeneity in the Veterans Administration, California Cancer Registry Geographically Matched Cohort, and the California Cancer Registry Fully Matched Cohorts
Due to data use restrictions, we were unable to analyze a pooled database of participants from the Veterans Administration and the California Cancer Registry.Therefore, we performed tests of heterogeneity using the Q-statistic with 1 degree of freedom for linear associations between nSES and each outcome (de novo metastasis, all-cause mortality, prostate cancer-specific mortality) 4 .We separately compared these associations in the Greater Los Angeles Veterans Administration to the Geographically matched California Cancer Registry and the Fully matched California Cancer Registry cohorts.Tests were two-sided with alpha=0.05.

Sensitivity analysis for unmeasured confounding
We compared associations between nSES, SIRE and PCa outcomes in the VA vs CCR to test the hypothesis that the health system in which a patient receives care can mitigate PCa outcome disparities associated with Black/African American race and nSES.However, unmeasured confounding arising from differences in patient-level factors (e.g., clinical factors, individual socioeconomic status) between the VA and CCR populations might also explain why disparities associated with nSES and SIRE are observed in CCR but not the VA.
The E-value is a statistical measure that has been proposed to quantify the minimum strength of association required by an unmeasured confounder with either the exposure (in this case, either Black/African American race or ADI) or outcome (in this case, metastatic PCa at diagnosis, ACM, or PCSM) to either shift the point estimate to the null value, or shift the confidence interval bound closest to the null to contain the null value, conditional on measured covariates 5 .Because epidemiologic associations are often reported on the ratio scale, the E-value can be interpreted as an odds or risk ratio for the association between the unmeasured confounding variable and either the exposure or outcome.Evalues are increasingly used in epidemiologic research to evaluate the robustness of reported associations to unmeasured confounding 6 .

E-values for unmeasured confounding
E-values for associations between NHB/AA and nSES are presented in eTable 4. The strongest Evalues observed were for associations between nSES Quintile 5 vs 1 for ACM (Estimate, Confidence Interval: 2.10, 1.96) and PCSM (Estimate, Confidence Interval: 1.82, 1.64).E-values for associations between NHB/AA and odds of metastatic PCa (1.61, 1.44), ACM (1.40, 1.20), and PCSM (1.44, 1.22) were moderately strong.These E-Values suggest that an unmeasured confounding variable would need have an association with PCa outcomes on the risk ratio scale ranging from 1.20-1.96to shift the confidence interval for associations of nSES or race and ethnicity with PCa outcomes to contain the null value of 1.It is plausible that unmeasured factors may exhibit relative risk ratios within the range of 1.20-1.96with outcomes, but these factors (particularly related to access to care) would lie along the causal path between health care system where care is received and PCa outcomes.These findings support our hypothesis that patients receiving care in the VA may receive health systems-level benefits that are associated with clinical characteristics at diagnosis and over follow-up, which then reduce the strength of associations of nSES and race and ethnicity with PCa outcomes.We interpret the "unmeasured confounding by clinical characteristics" seen in the CCR as evidence of differences in the healthcare system where patients in the VA seek care compared to the CCR, which translate to statistical differences in clinical presentation and outcomes over follow-up associated with race and nSES.The observation that confounding from unmeasured clinical factors may "explain" associations between race and nSES in the CCR provide support for the hypothesis that health systems factors that vary between the VA and other settings are a major contributor to observed racial disparities in PCa in population-based databases.

eTable 1. Characteristics of the Greater Los Angeles Veterans Administration Study Population Stratified by Race and Ethnicity (n=1,881)
2024 Wadhwa A et al.JAMA Network Open Chi-squared test of independence, b Wilcoxon Rank Sum Test, c Independent two-sample t-test, d Cell counts and percentages suppressed for confidentiality.(% or SD), [range].Percentages may not sum to 100 due to rounding.Abbreviations: NHB = Non-Hispanic Black, NHW = Non-Hispanic White, nSES = Neighborhood socioeconomic status, IQR = interquartile range © 2024 Wadhwa A et al.JAMA Network Open a

eTable 2. Characteristics of the California Cancer Registry Census Tract Matched Study Population Stratified by Race and Ethnicity (n=47,580)
Chi-squared test of independence, b Wilcoxon Rank Sum Test, c Independent two-sample t-test, (% or SD), [range].Percentages may not sum to 100 due to rounding.Abbreviations: NHB = Non-Hispanic Black, NHW = Non-Hispanic White, nSES = Neighborhood socioeconomic status, IQR = interquartile range a

Characteristics of the California Cancer Registry Cohort Matched to Greater Los Angeles Veterans Health Administration on Age, Race, Diagnosis Year, Stage, and Census Tract Stratified by Race and Ethnicity (n=2,914)
Chi-squared test of independence, b Wilcoxon Rank Sum Test, c Independent two-sample t-test, (% or SD), d Cell counts and percentages suppressed for confidentiality [range].Percentages may not sum to 100 due to rounding. a

eTable 5. Multivariable Analysis for Associations of Race and Ethnicity with Prostate Cancer Outcomes in the Veterans Health Administration Cohort and Fully Matched (Age, Race, Diagnosis Year, Stage, Census Tract) California Cancer Registry Cohort Non-Hispanic White Non-Hispanic Black
Odd ratios from Multivariable logistic regression analysis for de novo metastasis incidence, b Hazard ratios from Cox-Proportional Hazard Model for All-Cause Mortality was performed in the VA and CCR cohorts, c Subdistribution hazard ratios from Fine-Gray Analysis for Prostate Cancer Specific Mortality and multivariate logistic regression analysis for de novo metastasis incidence were performed in the VA and CCR cohorts.e Matched on age, race, state at diagnosis, year of diagnosis, and census tract.
a f P-value from q-statistic with 1 degree of freedom comparing heterogeneity of association in CCR Fully Matched cohort to GLA cohort.All analyses included the covariates of race, nSES at census tract level (quintiles), age at diagnosis, and year of diagnosis, urbanicity.Models for mortality further included stage at diagnosis.(SIRE = self-identified race/ethnicity, NHW = Non-Hispanic White, NHB/AA = Non-Hispanic Black, Other/Unk = All Other/Unknown, VA = Veterans Administration Cohort, CCR = California Cancer Registry Cohort) © 2024 Wadhwa A et al.JAMA Network Open eTable 6.

Multivariable Analysis for Associations of Neighborhood Socioeconomic Status with Prostate Cancer Outcomes in the Veterans Health Administration Cohort and Fully Matched (Age, Race, Diagnosis Year, Stage, Census Tract) California Cancer Registry Cohort
Per Interquartile Range, b Odds ratios from Multivariable logistic regression analysis for de novo metastasis incidence were performed in the VA and CCR cohorts, c Hazard ratios from Cox-Proportional Hazard Model for All-Cause Mortality was performed in the VA and CCR cohorts, d Subdistribution hazard ratios from Fine-Gray Analysis for Prostate Cancer Specific Mortality, e Matched on age, race, state at diagnosis, year of diagnosis, and census tract.f P-value from qstatistic with 1 degree of freedom comparing heterogeneity of association in CCR Fully Matched cohort to GLA cohort.All analyses included the covariates of race, nSES at census tract level, age at diagnosis, and year of diagnosis, urbanicity.Models for mortality further included stage at diagnosis.Abbreviations: SIRE = self-identified race/ethnicity, NHW = Non-Hispanic White, NHB/AA = Non-Hispanic Black, Other/Unk = All Other/Unknown, VA = Veterans Administration Cohort, CCR = California Cancer Registry Cohort) a